These two papers present an innovative method of configurable flaw classification and volume estimation in oil pipelines. In part I, the ultrasonic image acquisition system is introduced and surface and volume of the flaw are estimated with fuzzy image processing. A number of real figures illustrate the system performance. The flops calculation reveals that this fuzzy estimator could be integrated in a real time flaw detection system. In part II, at first, the dynamic detection of interesting points, i.e. as feature points at different levels of images, is proposed using wavelet transform. Furthermore, a guided searching strategy is used for the best matching from the coarse level to a fine level. Then, an error reduction neural network classifies the type of flaw using the wavelet transform as well as area and volume which were produced in Part I. Finally, a statistical error analyser verifies the system outputs by distinct inputs. The numerical results indicate that significant advantages are achieved over the other neural network based flaw characterisation schemes, whereas the accuracy of the predicted flaw profile can be controlled by the resolution of the network.
Configurable ultrasonic flaw classification of oil pipelines. Part II: Feature extraction, classification and error verification
Nondestructive Testing and Evaluation ; 23 , 2 ; 77-88
2008
12 Seiten, 5 Bilder, 6 Tabellen, 28 Quellen
Article (Journal)
English
Ultrasonic flaw detection during NDE of oil pipelines via a resonance filter
Tema Archive | 2006
|Engineering Index Backfile | 1952
Emerald Group Publishing | 1984